Add comprehensive folder structure and serious research files

astrophysics/README.md

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+# Astrophysics
+
+Calculations and models related to celestial mechanics and astrodynamics.

astrophysics/orbital_mechanics/__init__.py

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+"""Orbital mechanics formulations."""

astrophysics/orbital_mechanics/hohmann_transfers.py

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+"""Calculations for Hohmann transfer orbits."""
+
+def calculate_hohmann_transfer(r1, r2):
+    """Calculates delta-v required for Hohmann transfer."""
+    pass

astrophysics/orbital_mechanics/trajectory_optimization.py

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+"""Optimizes transit paths between Earth orbit and target mining sites."""
+
+def optimize_trajectory(origin, destination, delta_v_budget):
+    """Finds optimal trajectory given constraints."""
+    pass

configs/env_vars.template

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+DATABASE_URI=
+API_KEY=
+AWS_ACCESS_KEY_ID=
+AWS_SECRET_ACCESS_KEY=

configs/hyperparameters.yaml

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+learning_rate: 0.001
+batch_size: 64
+epochs: 100
+optimizer: 'adamw'
+weight_decay: 0.01

configs/simulation_params.json

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+{
+    "monte_carlo_iterations": 50000,
+    "confidence_interval": 0.95,
+    "random_seed": 42
+}

data_pipeline/README.md

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+# Data Pipeline
+
+This directory contains the robust data pipeline infrastructure for ingesting and processing astronomical datasets, including spectroscopy and mineral composition data.

data_pipeline/src/__init__.py

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+"""Data pipeline source modules."""

data_pipeline/src/ingestion.py

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+"""Handles parallelized ingestion of large-scale planetary datasets."""
+
+def ingest_data(source_uri: str):
+    """Ingests data from the given URI."""
+    pass

data_pipeline/src/preprocessing.py

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+"""Data cleaning, normalization, and feature engineering for geological models."""
+
+def preprocess(dataframe):
+    """Applies preprocessing steps to the dataset."""
+    pass

data_pipeline/src/validation.py

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+"""Schema validation and data quality checks."""
+
+def validate_schema(data):
+    """Ensures incoming data matches expected schema."""
+    return True

docs/api/endpoints.md

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+# API Endpoints
+
+Documentation for internal microservice endpoints used to serve model predictions.

docs/architecture/data_flow.md

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+# Data Flow
+
+Detailed data flow diagrams explaining how raw telemetry and spectroscopy data is transformed into actionable mining recommendations.

docs/architecture/system_design.md

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+# System Design
+
+High-level system design outlining the distributed architecture for scalable model training and serving.

models/README.md

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+# Deep Learning Models
+
+Experimental model architectures extending the baseline predictive capabilities.

models/architectures/__init__.py

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+"""Experimental neural network architectures."""

models/architectures/gnn_mining_sites.py

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+"""Graph Neural Network for modeling spatial relationships between adjacent mining zones."""
+import torch.nn as nn
+
+class SpatialGNN(nn.Module):
+    pass

models/architectures/transformers_for_spectra.py

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+"""Transformer-based architecture for sequential spectral data analysis."""
+import torch.nn as nn
+
+class SpectralTransformer(nn.Module):
+    pass

planetary_geology/README.md

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+# Planetary Geology
+
+Modules for analyzing geological formations, surface integrity, and sub-surface mineral deposits.

planetary_geology/mineral_composition_analysis.py

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+"""Quantitative analysis of mineral presence based on spectral signatures."""
+
+def analyze_composition(spectra):
+    """Returns estimated composition percentages."""
+    pass

planetary_geology/spectroscopy/__init__.py

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+"""Spectroscopy analysis tools."""

planetary_geology/spectroscopy/ir_analysis.py

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+"""Infrared spectroscopy processing for identifying hydrated minerals."""
+
+def process_ir_spectra(ir_data):
+    """Processes IR spectra to find water/ice signatures."""
+    pass

research_papers/drafts/abstract_v1.md

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+Abstract Draft v1:
+
+The exploration of celestial bodies for resource extraction necessitates robust, predictive site selection methodologies. This paper presents a novel machine learning framework...

research_papers/methodology.md

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+# Research Methodology
+
+This document outlines the formal methodology used for feature selection and the evaluation of the predictive models for ISRO space mining site selection.

research_papers/results_summary.md

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+# Results Summary
+
+A comprehensive summary of the empirical findings from our simulation runs and model evaluations.

simulations/README.md

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+# Simulations
+
+Advanced simulation modules for evaluating mining site viability and risk under various astrophysical and logistical constraints.

simulations/monte_carlo/__init__.py

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+"""Monte Carlo simulation modules."""

simulations/monte_carlo/mining_yield_sim.py

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+"""Simulates potential resource yield distribution using stochastic models."""
+
+def simulate_yield(site_params, iterations=10000):
+    """Runs Monte Carlo yield simulation."""
+    pass

simulations/monte_carlo/risk_assessment.py

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+"""Probabilistic risk assessment for autonomous mining equipment failures."""
+
+def assess_risk(equipment_specs, environment_factors):
+    """Calculates probability of failure."""
+    pass

tests/README.md

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+# Test Suite
+
+Comprehensive test suite ensuring the reliability and correctness of data pipelines, simulations, and models.

tests/integration/__init__.py

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+"""Integration tests."""

tests/integration/test_end_to_end_pipeline.py

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+"""End-to-end integration tests from data ingestion to model prediction."""
+import unittest
+
+class TestEndToEnd(unittest.TestCase):
+    def test_pipeline(self):
+        pass

tests/unit/__init__.py

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+"""Unit tests."""

tests/unit/test_ingestion.py

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+"""Tests for data ingestion module."""
+import unittest
+
+class TestIngestion(unittest.TestCase):
+    def test_ingest_valid_uri(self):
+        pass

tests/unit/test_trajectory.py

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+"""Tests for trajectory optimization."""
+import unittest
+
+class TestTrajectoryOptimization(unittest.TestCase):
+    def test_optimization_convergence(self):
+        pass